Klasifikasi Model Konten Kuliner Viral UMKM di TikTok Menggunakan Algoritma Naive Bayes
DOI:
https://doi.org/10.30865/jurikom.v13i1.9537Keywords:
TikTok, TF-IDF, Naïve Bayes, MSMEs, Classification, Text MiningAbstract
This study examines the development of a classification model for viral culinary content of Micro, Small, and Medium Enterprises on the TikTok platform based on video caption text. The main problem lies in the high variation of promotional language, the use of trending terms, and unstructured text formats, which make the identification of viral culinary categories difficult to perform manually and inconsistently. This study aims to design a systematic classification model to automatically and measurably group TikTok captions of enterprises into viral culinary categories. The dataset consists of 800 captions collected through scraping using the Apify API. The model development process includes preprocessing stages such as cleaning, case folding, tokenizing, normalization, stopword removal, stemming, and detokenization to produce standardized text. Feature weighting is then performed using TF-IDF, followed by model construction using the Naïve Bayes algorithm. The resulting model classifies data into ten viral culinary categories, namely Donat Mochi, Cireng, Risol, Kentang Curly, Lukchup, Dimsum Mentai, Es Teh Jumbo, Indomie Telur, Mochi Daifuku, and Dessert Box. Evaluation using a confusion matrix and classification report shows an accuracy of 0.74 or 74 percent. These results indicate the model supports automated analysis of viral culinary trends.
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